All You Need to Know About Categorical Data in ML

Did you know? The concept of encoding categorical data in machine learning dates back to the early 1960s, when it was developed to convert text-based categories into numerical representations for computational models. This transformation, initially done manually, now plays a key role in modern machine learning pipelines, enabling models to handle diverse datasets efficiently.

Categorical data in machine learning refers to variables that represent categories rather than numerical values. These variables need to be converted into a format that machine learning models can process, a process known as encoding. 

Proper handling of categorical data in ML is crucial, as it can significantly impact model accuracy and performance.

In this guide, you’ll learn how to work with categorical data in machine learning, including various encoding methods, challenges, and best practices for improved model outcomes.

Improve your machine learning skills with our online AI and ML courses. Learn how to handle and encode data variables efficiently in ML models! 

What is Categorical Data in ML? Simple Explanation

Categorical data in ML refers to variables that represent categories rather than numerical values. These categories can be divided into two main types:

• Nominal Data: These are categories that do not have a specific order or ranking. Examples include color, gender, or country. The values are purely labels and cannot be mathematically ordered.
• Ordinal Data: These categories have a meaningful order, but the distances between the categories are not consistent. Examples include ratings (poor, good, excellent) or educational levels (high school, bachelor’s, master’s, PhD).

Handling categorical data is essential in machine learning because many algorithms are designed to process numerical values. Categorical data needs to be converted into a numerical format for the model to interpret it effectively. 

For example, consider a dataset of customer preferences in an e-commerce platform where "product preference" is categorical, such as "electronics," "clothing," and "furniture." A model cannot directly use these categories, so they need to be encoded into a numerical format, such as assigning 1 for electronics, 2 for clothing, and 3 for furniture.

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By understanding the nature of categorical data in ML and using appropriate encoding methods, you can improve the accuracy and efficiency of your machine learning models.

Also Read: 4 Types of Data: Nominal, Ordinal, Discrete, Continuous | upGrad blog

Now, let’s understand how you can encode the categorical data in ML using different methods.

Which Method is Used for Encoding the Categorical Variables in ML? Types

When working with machine learning categorical data, encoding is essential to convert categories into numerical values for model interpretation. The choice of encoding method significantly impacts performance. One-Hot Encoding works well for non-ordinal categories but can create high-dimensionality, while Label Encoding is memory-efficient but may introduce unintended ordinal relationships. 

Target Encoding is useful when categorical features have a relationship with the target variable, but it must be carefully handled to prevent data leakage. Frequency Encoding captures category distribution and is valuable when category frequencies matter. Choosing the right encoding method based on the feature structure and model requirements enhances learning and generalization.

Below are the most common encoding methods used for machine learning categorical data. 

 1. One-Hot Encoding

One-hot encoding creates a new binary column for each possible category in the feature. Each column represents a category, and a '1' is placed in the column corresponding to the category of a data point, while the rest are '0'.

When to use? One-hot encoding is ideal for nominal data, where there is no inherent order or ranking between the categories, such as "color" (red, green, blue) or "city" (New York, London, Paris). It ensures that the model treats each category as a separate entity without assuming any order.

Why it's used? One-hot encoding is used to prevent the model from assuming any ordering or hierarchy between categories. This is particularly useful for nominal data, where categories are independent of each other. However, one-hot encoding should not be used for ordinal data (such as "low", "medium", "high") because ordinal categories have a meaningful order. 

One-hot encoding doesn't preserve this order, potentially causing the model to treat the data as if all categories were independent and equally distant from one another, which could negatively impact model performance. Ordinal data should be encoded in a way that retains the inherent order, such as using label encoding or ordinal encoding.

How to Implement in Python?

import pandas as pd

# Sample data
data = {'Color': ['Red', 'Green', 'Blue', 'Green', 'Blue']}
df = pd.DataFrame(data)

# Apply One-Hot Encoding using pandas get_dummies
one_hot_encoded_df = pd.get_dummies(df, columns=['Color'])

print("One-Hot Encoded DataFrame:")
print(one_hot_encoded_df)

Code Explanation: pd.get_dummies() is used to perform one-hot encoding on the "Color" column. It creates a new column for each unique color (Red, Green, Blue) and assigns 1 or 0 based on the presence of that category in each row.

Output:

One-Hot Encoded DataFrame:
  Color_Blue  Color_Green  Color_Red
0           0            0          1
1           0            1          0
2           1            0          0
3           0            1          0
4           1            0          0

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2. Label Encoding

Label encoding assigns each category a unique integer value. For example, "red" might be encoded as 0, "green" as 1, and "blue" as 2.

When to use? Label encoding is most effective for ordinal data, where there is a meaningful order between the categories, such as "low," "medium," and "high." In these cases, encoding allows the model to interpret the ordered nature of the categories.

Why it's used? Label encoding is beneficial because it represents the categories numerically without creating additional features, as is done with one-hot encoding. However, it can introduce an unintended ordinal relationship when used on nominal (non-ordered) data, where the categories have no inherent ranking.

In such cases, label encoding might negatively impact models that assume a numerical ranking, such as linear regression or tree-based models. Therefore, label encoding is typically reserved for ordinal data, not nominal data.

How to Implement in Python?

from sklearn.preprocessing import LabelEncoder

# Sample data
data = {'Color': ['Red', 'Green', 'Blue', 'Green', 'Blue']}
df = pd.DataFrame(data)

# Initialize LabelEncoder
label_encoder = LabelEncoder()

# Apply Label Encoding
df['Color_encoded'] = label_encoder.fit_transform(df['Color'])

print("Label Encoded DataFrame:")
print(df)

Code Explanation: LabelEncoder from sklearn.preprocessing is used to assign a unique integer to each category in the "Color" column. The fit_transform() method is used to encode the categorical values into integer labels.

Output:

Label Encoded DataFrame:
  Color  Color_encoded
0    Red              2
1  Green              1
2   Blue              0
3  Green              1
4   Blue              0

3. Binary Encoding

Binary encoding converts categories into binary digits. Each category is first assigned a unique integer, which is then converted into binary code. For example, category "red" might become 001, "green" 010, and "blue" 011.

When to use? Binary encoding is typically used when the number of categories is large, and one-hot encoding would result in a very sparse matrix with many columns.

Why it's used? It reduces the dimensionality compared to one-hot encoding while still preserving information about the categories. It's a good middle ground when you have high-cardinality features.

How to Implement in Python?

import category_encoders as ce

# Sample data
data = {'Color': ['Red', 'Green', 'Blue', 'Green', 'Blue']}
df = pd.DataFrame(data)

# Initialize BinaryEncoder
binary_encoder = ce.BinaryEncoder(cols=['Color'])

# Apply Binary Encoding
binary_encoded_df = binary_encoder.fit_transform(df)

print("Binary Encoded DataFrame:")
print(binary_encoded_df)

Code Explanation: We use category_encoders.BinaryEncoder to convert the categories into binary code. The cols parameter specifies the column to be encoded. 

The method generates binary values for each category, reducing dimensionality compared to one-hot encoding.

Output:

Binary Encoded DataFrame:
  Color_0  Color_1
0        0        1
1        1        0
2        0        0
3        1        0
4        0        0

4. Frequency Encoding

Frequency encoding assigns each category the frequency or count of its occurrences in the dataset. For example, if "red" appears 100 times, "green" appears 50 times, and "blue" appears 30 times, they would be encoded as 100, 50, and 30, respectively.

When to use? This method is often used when the frequency of the categories carries significance. It works well with high-cardinality features but might not be appropriate when the frequency distribution is skewed.

Why it's used? It’s simple and efficient, reducing dimensionality without creating additional features. However, it assumes that the frequency of the category has predictive value, which might not always be true.

How to Implement in Python?

# Sample data
data = {'Color': ['Red', 'Green', 'Blue', 'Green', 'Blue']}
df = pd.DataFrame(data)

# Frequency Encoding
frequency_encoding = df['Color'].value_counts().to_dict()
df['Color_encoded'] = df['Color'].map(frequency_encoding)

print("Frequency Encoded DataFrame:")
print(df)

Code Explanation: value_counts() counts the occurrences of each category in the "Color" column. map() is then used to replace each category in "Color" with its corresponding frequency.

Output:

Frequency Encoded DataFrame:
  Color  Color_encoded
0    Red              1
1  Green              2
2   Blue              2
3  Green              2
4   Blue              2

 5. Target Encoding

Target encoding replaces each category with the mean of the target variable for that category. For example, in a dataset predicting house prices, the "neighborhood" feature might be encoded with the average price of houses in each neighborhood.

When to use? This method is particularly useful when there’s a clear relationship between the category and the target variable, like in regression tasks.

Why it's used? It often leads to better model performance by incorporating the target variable’s relationship with the categories. However, it should be used with caution to avoid data leakage and overfitting, especially in cases where the categories are closely related to the target variable.

How to Implement in Python?

import pandas as pd

# Sample data
data = {'Color': ['Red', 'Green', 'Blue', 'Green', 'Blue'],
        'Price': [100, 200, 150, 250, 175]}
df = pd.DataFrame(data)

# Target Encoding
mean_price = df.groupby('Color')['Price'].mean().to_dict()
df['Color_encoded'] = df['Color'].map(mean_price)

print("Target Encoded DataFrame:")
print(df)

Code Explanation: We calculate the mean value of the target variable (Price) for each category in the "Color" column using groupby() and mean(). The map() function replaces the categories in "Color" with the mean price.

Output:

Target Encoded DataFrame:
  Color  Price  Color_encoded
0    Red    100          100.0
1  Green    200          225.0
2   Blue    150          162.5
3  Green    250          225.0
4   Blue    175          162.5

Choosing the right encoding method based on the type of categorical data (nominal or ordinal) and the dataset's characteristics will help you enhance the performance of machine learning models.

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Also Read: Top 5 Machine Learning Models Explained For Beginners

Now that you know which method is used for encoding the categorical variables in ML, let’s look at some of the best practices to handle data in machine learning.

Best Practices to Handle Categorical Data in Machine Learning

How you encode, process, and treat categorical features directly influences your model's ability to generalize and make accurate predictions. Incorrect encoding or handling can introduce biases, lead to inefficient use of memory, and even cause models to misinterpret data, which impacts overall performance. 

By applying the correct encoding methods and strategies, you ensure that your model learns from meaningful, consistent, and appropriately represented categorical data.

Below are some best practices that ensure the data is properly prepared for modeling and to avoid common pitfalls like overfitting, bias, or incorrect predictions:

1. Choose the Appropriate Encoding Method

Selecting the right encoding method is essential because different encoding strategies work better for different types of categorical data. For instance, One-Hot Encoding creates binary columns for each category and is effective for models that require numerical input, but can be inefficient with high-cardinality features. 

Label Encoding assigns integers to categories and is ideal for ordinal data but can be misleading for models that treat integers as continuous values. By selecting the appropriate encoding, you ensure that your model interprets the data correctly without introducing bias or misrepresentation.

2. Handle Missing Values Carefully

Missing values are inevitable in real-world data, and improper handling can introduce significant errors. Imputing missing values improperly may lead to models that learn from incomplete data, affecting the generalization ability. 

Using strategies like imputing the most frequent category or using predictive imputation methods ensures that your model is trained on as much relevant data as possible without introducing unrealistic assumptions or bias.

3. Ensure Consistency in Categories

Inconsistent categories across training and testing datasets can drastically impact model performance, leading to incorrect predictions. For example, if a new category appears in the test set that was never seen in the training set, it may be treated as an outlier or incorrectly classified. 

Ensuring consistency prevents this by aligning category values in both datasets, avoiding unpredictable behavior when the model is deployed.

4. Be Aware of High-Cardinality Features

Categorical features with a large number of categories can result in sparse matrices, increasing computational costs and potentially leading to overfitting. Target Encoding or Frequency Encoding can reduce dimensionality while maintaining essential information. 

By carefully choosing your encoding method for high-cardinality features, you prevent your model from becoming unnecessarily complex, improving its performance and efficiency.

5. Review Feature Relationships

Target Encoding works by using the target variable’s mean to encode categories, which could lead to data leakage if not applied correctly. By ensuring that encoding is done in a way that respects the separation between training and testing datasets, you prevent the model from "cheating" by learning information from the target variable prematurely. 

Regularly reviewing the relationship between features helps ensure that your categorical data is both informative and reliable without introducing biases.

Handling categorical data correctly allows you to extract more value from your features, resulting in models that are both reliable and efficient.

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Also Read: How to Implement Machine Learning Steps: A Complete Guide

Now that you know the best practices for handling categorical data in machine learning, let’s look at some of the applications of encoding categorical data.

What are the Use Cases of Encoding Categorical Data in ML?

Industries rely on encoding methods to ensure that categorical features are transformed in a way that captures their underlying relationships, optimizes model performance, and improves predictive accuracy. 

For example, in customer segmentation, encoding enables algorithms to identify patterns in demographic data, leading to more targeted marketing strategies. In healthcare, encoding allows models to predict patient outcomes based on medical conditions and treatment histories. 

Here are some key use cases of encoding categorical data in ML:

1. Customer Segmentation in Marketing

Imagine you're working in marketing for an e-commerce company. You want to segment your customers based on demographics (age, location, gender) and their purchasing behavior (product categories, purchase frequency). You know that machine learning models need numerical data to generate meaningful insights, so you decide to encode the categorical variables.

You apply One-Hot Encoding for categorical variables like gender and location, turning each category into a binary column. For the purchase frequency, you use Frequency Encoding, representing how often each customer falls into a certain purchase frequency bracket.

Outcome: With this encoding strategy, your segmentation model effectively groups customers into distinct categories. You can now create highly targeted campaigns for each segment, which improves customer engagement and increases conversion rates.

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2. Predicting Disease Outcome in Healthcare

As a data scientist in a healthcare organization, you're tasked with predicting patient outcomes based on medical history and lifestyle factors, such as smoking, diet, and exercise habits. Some of these features are categorical (e.g., smoker vs. non-smoker, healthy vs. unhealthy diet), and you need to encode them into numerical values for your machine learning model.

You choose Target Encoding for variables like diet and exercise habits, which assigns each category a value based on the mean outcome of the target variable. For simpler features like smoking status, you use Label Encoding to convert "smoker" and "non-smoker" into binary numeric values.

Outcome: With the encoded features, the model successfully predicts the likelihood of disease progression with greater accuracy. This leads to better-targeted treatments and more effective health interventions, improving patient outcomes and optimizing healthcare resources.

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3. Fraud Detection in Finance

Imagine you're working on a fraud detection system for an online banking platform. The dataset includes categorical variables like transaction type (e.g., withdrawal, transfer, deposit) and user location (e.g., city, country), and you need to encode them so your machine learning algorithm can detect fraudulent behavior.

You apply One-Hot Encoding to the transaction type and Label Encoding for user location. This allows the model to distinguish between different transaction types and analyze location-based patterns in fraudulent activities.

Outcome: After training the model, the system identifies fraudulent transactions more accurately, reducing the number of false positives and improving security. The bank can now flag suspicious activities in real time, reducing fraud risk and improving customer trust.

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4. Product Recommendations in Retail

You're working as a data scientist for a retail chain, and you want to build a product recommendation engine based on customer purchase history and product category. The data includes categorical variables like product type and customer membership status (e.g., regular, premium).

You use Target Encoding for customer membership status to link the likelihood of purchasing a product based on membership. For product types, you choose One-Hot Encoding to create a binary representation of each category, ensuring the model can identify patterns based on product preferences.

Outcome: Your recommendation system now suggests products that customers are most likely to buy, based on their membership status and previous purchases. This leads to higher conversion rates, a personalized shopping experience, and increased sales.

5. Churn Prediction in E-commerce

As a data analyst for an e-commerce platform, you are tasked with predicting which customers are likely to churn (leave the platform). The dataset contains categorical features like subscription plan (e.g., free, basic, premium) and customer support interactions (e.g., contacted, not contacted).

You apply Label Encoding to the subscription plan, converting free, basic, and premium into numeric labels. For customer support interactions, you use Frequency Encoding, where you represent each interaction type by the frequency of that interaction across the dataset.

Outcome: Your churn prediction model now identifies high-risk customers with better accuracy, allowing the marketing team to target them with retention offers. The result is a reduction in churn rates and an increase in customer retention and satisfaction.

These case studies show how different encoding methods can be applied to categorical data to unlock valuable insights and improve decision-making in various industries.

You can also enroll in upGrad’s free Data Science in E-Commerce course. After completing the course, you gain a solid understanding of recommendation systems, price optimization, market mix modeling, and A/B testing to drive sales and enhance customer experience.

Also Read: Exploring the Scope of Machine Learning: Trends, Applications, and Future Opportunities

To solidify your understanding of the categorical data in ML, test your knowledge with a quiz. It’ll help reinforce the concepts discussed throughout the tutorial and ensure you're ready to apply them in your projects.

Quiz to Test Your Knowledge on Categorical Data in ML

Assess your understanding of categorical data, encoding methods, best practices, and real-world applications in machine learning by answering the following multiple-choice questions.

Test your knowledge now!

1. What is the primary goal of encoding categorical data in machine learning? 
a) To convert numerical data into categorical labels
b) To transform categorical data into a format that can be processed by ML algorithms
c) To reduce the number of unique categories
d) To handle missing values in categorical data

2. Which of the following encoding methods is most suitable for converting nominal categorical data, such as "red", "blue", and "green" (without any natural order)? 
a) One-Hot Encoding
b) Label Encoding
c) Frequency Encoding
d) Target Encoding

3. When should you consider using Target Encoding for categorical data?
a) When the categorical data has no relation to the target variable
b) When the categorical feature has a large number of categories
c) When you have only binary categorical features
d) When your dataset is small and doesn’t require performance optimization

4. Which encoding method is most useful when you have an ordinal categorical variable, such as "low", "medium", and "high"?
a) One-Hot Encoding
b) Label Encoding
c) Binary Encoding
d) Frequency Encoding

5. What is a significant downside of Label Encoding?
a) It works only for binary categorical variables
b) It introduces unintended ordinal relationships in nominal data
c) It leads to overfitting in decision tree models
d) It cannot be used for non-numerical data

6. What does Frequency Encoding do?
a) It assigns numeric labels to each category based on frequency
b) It replaces categories with their frequency of occurrence in the dataset
c) It encodes categories based on their mean target value
d) It assigns a unique integer to each category

7. Why is One-Hot Encoding preferred over Label Encoding in some cases?
a) It reduces computational cost
b) It prevents models from making assumptions about the categories
c) It preserves the ordinal relationship in the data
d) It is faster for large datasets

8. Which method is recommended when encoding categorical data with many categories that don’t have a natural order and would cause a large number of new features after One-Hot Encoding?
a) Label Encoding
b) Binary Encoding
c) Frequency Encoding
d) Target Encoding

9. How can improper encoding of categorical data affect machine learning models? 
a) It can cause models to overfit to the data
b) It can result in incorrect predictions due to misinterpreted categories
c) It can make models slower to train
d) All of the above

10. What is a key benefit of handling categorical data correctly before feeding it into machine learning algorithms?
a) It ensures that the model will always perform well regardless of the data
b) It enables the model to learn from the data efficiently and make accurate predictions
c) It reduces the complexity of the model without affecting accuracy
d) It ensures the dataset is always balanced and free of bias

This quiz will help you evaluate your understanding of categorical data, encoding techniques, and their applications in machine learning. 

Also Read: 5 Breakthrough Applications of Machine Learning

You can also continue expanding your skills in machine learning with upGrad, which will help you deepen your understanding of advanced ML concepts and real-life applications.

Upskill with upGrad to Stay Ahead of Industry Trends! 

upGrad’s courses provide expert training in machine learning, with a focus on categorical data and encoding methods, their practical applications, and best practices. Learn how to optimize your machine learning models for different scenarios.

While the course covered in the tutorial can significantly improve your knowledge, here are some free courses to facilitate your continued learning:

• Hypothesis Testing
• Unsupervised Learning: Clustering
• Logistic Regression for Beginners
• Linear Regression - Step by Step Guide
• Introduction to Natural Language Processing

You can also get personalized career counseling with upGrad to guide your career path, or visit your nearest upGrad center and start hands-on training today! 

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